Benchmarking taxonomic classifiers for metagenomics using nanopore sequencing

Sammanfattning

Metagenomic Next Generation Sequencing is increasingly being adopted for clinical diagnostic use as a valuable complement to traditional methods of pathogen detection. After major improvements in accuracy, Oxford Nanopore sequencing represents a viable alternative to Illumina. With longer read lengths, lower cost and shorter turnaround times, Nanopore can reduce the time to diagnosis and improve patient outcomes. To realize this potential, sensitive taxonomic classifiers with support for long-read data are needed. In this study we benchmark the performance of different taxonomic classifiers on Nanopore-sequenced viral data from both mock and real clinical samples. Custom databases for the Kraken2, DIAMOND, Metabuli, MetaCache and Sylph classifiers were built from the same reference sequence data. Classifications were compared to BLAST alignments as a gold standard and the classifiers were evaluated in terms of sensitivity and precision. Metabuli and MetaCache were the most sensitive across datasets and for different viruses, at the cost of long processing times and high memory requirements respectively. Kraken2 showed excellent precision but was the least sensitive. DIAMOND performed well on the mock data but had lower species-level sensitivity on the shorter read length clinical data, likely reflecting the limited specificity of protein-based classifiers. Sylph was highly computationally efficient and in combination with Minimap2 performed well for most viruses, but was unable to detect some low-coverage genomes at default thresholds. Given enough memory, MetaCache may be the most suitable classifier for a diagnostic workflow, possibly in combination with DIAMOND to leverage the benefits of both DNA- and protein-based classifiers.